BART (Benchmarking Against Random Trees)

BART, or Benchmarking Against Random Trees, is a method used to evaluate the performance of different algorithmic approaches, particularly in machine learning and data analysis. It involves comparing a model’s predictions to those made by a random selection of simpler models (like decision trees). By gauging how well a complex model performs relative to these random trees, researchers can determine its effectiveness and reliability. This benchmarking helps in understanding the strengths and weaknesses of different models, ensuring that the best methods are chosen for analyzing data and making predictions.